The mahalanobis taguchi system—adaptive resonance theory neural network algorithm for dynamic product designs

Abstract This work presents a novel algorithm, which combines the Mahalanobis Taguchi system (MTS) with the adaptive resonance theory neural network (ARTN) method for parameter selections in a dynamic product design system (DPDS). The utility of the algorithm is assessed in two dimensions: the MTS shows how individual product parameter dimensions are selected, and the ARTN links parameter selection decisions across two different timelines and can be used to focus on DPDS and to identify product architecture dimensions that are critical for a DPDS. The MTS can easily solve product parameter selection problems and has proved to be computationally efficient in previous studies. Additionally, the ARTN algorithm provides a simple and efficient means of constructing a DPDS, which is verified by the ARTN algorithm and is presented in this study. From the results of this study, we conclude that the MTS-ARTN algorithm can be applied successfully in a DPDS environment.